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TV move backwards use case

TV move backwards use case

Global use case developed by Aura Platform Team that allows users to move the content backwards on the TV by using Aura

Introduction

The TV move backwards use case is a global experience designed and developed by Aura Global Team that allows Telefónica customers to move the content backwards on the TV using a vocal interface.

Find additional information in following the documents:

Specifications

Request-response model

TV move backwards use case is adapted to the new request-response normalized model v3.

Available channels

Once TV move backwards v3 use case is fully normalized, it will be available for any channel that implements normalized v3 request-response model including TV related data.

Currently, STB channel in ES is available for channelData V1. And channelData V3 is suitable for STB in Brazil with time entity recognition: ent.time_length_hour.

Display features

Currently, the normalized TV move backwards use case includes basic move backwards features for some entities:

  • Move the TV content backwards
  • Move backwards by specifying the desired time

This UC also includes specific verbs without entities. For AP repository the key verbs are: “retroceder”, “retornar” and “rebobinar”. For Brazil, they are “recuar” and “retroceder”.

Therefore, the answer could be of this type: “Okay, I’ll rewind the TV content.”

Use case development

The TV move backwards use case development includes these components:

Understanding features

  • TV move backwards use case intent: intent.tv.move_backwards

  • TV move backwards use case entities:

Entity Example
ent.audiovisual_genre “Move the movie backwards”
ent.time_length_hour “Move the movie backwards 2 hours”
ent.time_length_min “Move the movie backwards 2 minutes”
ent.time_length_sec “Move the movie backwards 2 seconds”
ent.time_instant “Move it backwards now”

⚠️ Other entities in the user’s request are not taken into account in the move backwards process.

In order to understand users’ requests (utterances), Aura is trained with:

  • NLP expression to recognize the user’s utterance and detect the user’s intention.

Use case logic

Once Aura has recognized the user’s utterance based on NLP components, the use case should be resolved based on:

1 - Dialog configuration

Configuration of the generic dialog for TV move backwards use case

How the generic dialog must be configured for this use case

Specific configuration

The TV move backwards use case is triggered by the generic dialog v3. For this use case, the required specific configuration is set below:

  • Configuration for Brazil OB in STB channel. In this configuration, no default value is defined, as the STB device will resolve it when receiving an empty value in the parameter levels. A new entity is now recognized: ent.time_length_hour. Additional recognition was required by the aura-bot in order to obtain the total number of seconds to be returned in the parameter levels. To obtain that convertion into seconds, a new function has been created in utiles called: getSecondsLevelsWithTimerRecognizer.
{
    "id": "generic-dialog-v3",
    "channelDataVersion": "v3",
    "suggestions": false,
    "triggerConditions": [
        {
            "intent": "intent.tv.move_backwards",
            "settings": {
                "locales": {
                    "success": [
                        "tv:tv.moveBackwards"
                    ],
                    "error": [
                        "core:flc.answer.error"
                    ]
                },
                "actions": [
                    {
                        "name": "SEND_KEY"
                    },
                    {
                        "name": "MAKE_SOUND"
                    }
                ],
                "payloadType": "tv",
                "functions": {
                    "params": [
                        "currentIntent",
                        "persistentData",
                        "genericUtils",
                        "configuration"
                    ],
                    "algorithm": "return new Promise(async (resolve, reject) => { try { const params = { levels: await genericUtils.getSecondsLevelsWithTimerRecognizer(currentIntent, configuration) }; persistentData.dialogData.actionParams['SEND_KEY'] = params; resolve(); } catch (error) { reject(error); } });"
                }
            }
        }
    ],
    "onlyIn": [
        "set-top-box"
    ]
}

2 - TV move backwards resources

TV move backwards use case resources

Resources used by the TV move backwards use case

The TV move backwards use case will use two different types of resources:

3 - NLP configuration by OB

NLP configuration by OB

NLP configuration by OB for the TV move backwards use case

Introduction

The current document includes the configuration of the NLP model for the TV move backwards use case in the OBs where it is available.

Brazil

The aura-nlpdata repository structure in Brazil is organized by channel: aura-nlpdata-br-[channel]. Each repository will contain the files for the available use cases in this channel.

The following configuration corresponds to the STB channel, which is included in the repository: https://github.com/Telefonica/aura-nlpdata-br-stb

Intent

intent.tv.move_backwards

Entities

  • ent.audiovisual_genre
  • ent.time_length_sec
  • ent.time_length_min
  • ent.time_length_hour
  • ent.time_instant

Pipeline stages

The following stages of STB’s pipeline have been used to develop this UC:

  • PygrapeGrammarWrapper
  • EntityTaggerAdapterWrapper

The whole pipeline.jsonfile is available at: https://github.com/Telefonica/aura-nlpdata-br-stb/blob/master/data/pt-br/stb/pipeline.json